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Faster algorithms for learning to link, align sequences, and price two-part tariffs. (arXiv:2204.03569v1 [cs.DS])
April 8, 2022, 1:12 a.m. | Maria-Florina Balcan, Christopher Seiler, Dravyansh Sharma
cs.LG updates on arXiv.org arxiv.org
Data-driven algorithm configuration is a promising, learning-based approach
for beyond worst-case analysis of algorithms with tunable parameters. An
important open problem is the design of efficient data-driven algorithms for
algorithm families with more than one parameter. In this work we provide
algorithms for efficient (output-polynomial) multidimensional parameter tuning,
i.e. for families with a small constant number of parameters, for three very
different combinatorial problems -- linkage-based clustering, dynamic
programming for sequence alignment, and auction design for two-part tariff
schemes. We …
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